广义大气Rosenbluth方法（GARM）
Generalised Atmospheric Rosenbluth Methods (GARM)
摘要：
我们发现，经典的Rosenbluth方法采样自避免散步可以扩展到一个一般的算法，采样许多家庭的对象，包括自避免多边形。该实现依赖于一个基本的移动，这是一个概括的动力学增长，而不是只附加边缘的端点，边缘可以插入在任何顶点提供的结果对象仍然位于同一个家庭。我们实现了这种方法，使用修剪和丰富的样本自我避免散步和多边形。该算法可以通过将其与长度保持移动（例如枢轴和曲轴移动）混合来进一步扩展。
英文摘要：
We show that the classical Rosenbluth method for sampling self-avoiding walks can be extended to a general algorithm for sampling many families of objects, including self-avoiding polygons. The implementation relies on an elementary move which is a generalisation of kinetic growth; rather than only appending edges to the endpoint, edges may be inserted at any vertex providing the resulting objects still lie within the same family. We implement this method using pruning and enrichment to sample self-avoiding walks and polygons. The algorithm can be further extended by mixing it with length-preserving moves, such pivots and crank-shaft moves.
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